Data Science
Open in Telegram
Learn how to analyze data effectively and manage databases with ease. Buy ads: https://telega.io/c/sql_databases
Show more📈 Analytical overview of Telegram channel Data Science
Channel Data Science (@sql_databases) in the English language segment is an active participant. Currently, the community unites 70 805 subscribers, ranking 2 274 in the Education category and 4 582 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 70 805 subscribers.
According to the latest data from 25 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -309 over the last 30 days and by -33 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 12.05%. Within the first 24 hours after publication, content typically collects 2.78% reactions from the total number of subscribers.
- Post reach: On average, each post receives 8 533 views. Within the first day, a publication typically gains 1 972 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
- Thematic interests: Content is focused on key topics such as database, learning, linkedin, udemy, 029k|.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Learn how to analyze data effectively and manage databases with ease.
Buy ads: https://telega.io/c/sql_databases”
Thanks to the high frequency of updates (latest data received on 26 August, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.
70 805
Subscribers
-3324 hours
-1127 days
-30930 days
Posts Archive
70 803
🔅 Introduction to PostgreSQL
📝 Get an introduction to PostgreSQL—what it is, what it can do, and how to start using it.
🌐 Author: Sarah Conway Schnurr
🔰 Level: Beginner
⏰ Duration: 48m
📋 Topics: PostgreSQL
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70 803
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Unlocking the power of data analysis starts with understanding its foundation. Dive deep with me into the most pivotal distributions every data scientist should have in their toolkit. From Gaussian to Binomial, knowing these distributions is a game-changer in the realm of Data Science.
70 803
🔅 Top Five Things to Know in Advanced SQL
📝 Learn advanced SQL concepts and practice them with hands-on exercises.
🌐 Author: Kendall Ruber
🔰 Level: Advanced
⏰ Duration: 1h 35m
📋 Topics: SQL
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70 803
📖 Top 10 Database Scaling Techniques You Should Know:
1. 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠: Create indexes on frequently queried columns to speed up data retrieval.
2. 𝐕𝐞𝐫𝐭𝐢𝐜𝐚𝐥 𝐒𝐜𝐚𝐥𝐢𝐧𝐠: Upgrade your database server by adding more CPU, RAM, or storage to handle increased load.
3. 𝐂𝐚𝐜𝐡𝐢𝐧𝐠: Store frequently accessed data in-memory (e.g., Redis, Memcached) to reduce database load and improve response time.
4. 𝐒𝐡𝐚𝐫𝐝𝐢𝐧𝐠: Distribute data across multiple servers by splitting the database into smaller, independent shards, allowing for horizontal scaling and improved performance.
5. 𝐑𝐞𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Create multiple copies (replicas) of the database across different servers, enabling read queries to be distributed across replicas and improving availability.
6. 𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Fine-tune SQL queries, eliminate expensive operations, and leverage indexes effectively to improve execution speed and reduce database load.
7. 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧 𝐏𝐨𝐨𝐥𝐢𝐧𝐠: Reduce the overhead of opening/closing database connections by reusing existing ones, improving performance under heavy traffic.
8. 𝐕𝐞𝐫𝐭𝐢𝐜𝐚𝐥 𝐏𝐚𝐫𝐭𝐢𝐭𝐢𝐨𝐧𝐢𝐧𝐠: Split large tables into smaller, more manageable parts (partitions), each containing a subset of the columns/features from the original table.
9. 𝐃𝐞𝐧𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Store data in a redundant but structured format to minimize complex joins and speed up read-heavy workloads.
10. 𝐌𝐚𝐭𝐞𝐫𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐕𝐢𝐞𝐰𝐬: Pre-compute and store results of complex queries as separate tables to avoid expensive recalculation, reducing database load and improving response times.
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🔅 Python Data Structures: Dictionaries
📝 Learn how to use dictionaries to store and retrieve unordered data in Python.
🌐 Author: Deepa Muralidhar
🔰 Level: Beginner
⏰ Duration: 57m
📋 Topics: Data Structures, Python
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70 803
🔰 The 4 Types of SQL Joins
SQL joins combine rows from two or more tables based on a related column. Here are the different types of joins you can use:
1⃣ Inner Join
Returns only the matching rows between both tables. It keeps common data only.
🔢 Left Join
Returns all rows from the left table and matching rows from the right table. If a row in the left table doesn’t have a match in the right table, the right table’s columns will contain NULL values in that row.
🔢 Right Join
Returns all rows from the right table and matching rows from the left table. If no matching record exists in the left table for a record in the right table, the columns from the left table in the result will contain NULL values.
🔢 FULL OUTER JOIN
Returns all rows from both tables, filling in NULL for missing matches.
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🔅 Advanced NoSQL for Data Science
📝 Explore the fundamentals of NoSQL. Learn the differences between NoSQL and traditional relational databases, discover how to perform common data science tasks with NoSQL, and more.
🌐 Author: Dan Sullivan
🔰 Level: Advanced
⏰ Duration: 1h 54m
📋 Topics: Data Science, NoSQL
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